用大模型+外部工具自动生成药物靶点档案，提升准确性与实用性。
SwiftDossier: Tailored Automatic Dossier for Drug Discovery with LLMs and Agents
- 结合RAG增强大模型知识，减少事实错误
- 自动调用数据库与代码执行，生成完整靶点档案
- 输出可直接使用的PDF和PPT，适合药企研发人员
人工智能算法的进展已拓展至生物医学领域。大型语言模型（LLMs）在耗时且昂贵的药物发现过程中具有显著优势，但其自身缺乏特定领域的深度知识，易产生事实性错误，且无法使用外部工具执行复杂操作。本文针对这两个问题提出解决方案：首先，通过实现先进的检索增强生成（RAG）系统，显著提升大模型回答药物发现相关问题的准确性；实验表明，启用RAG后的生成结果优于无RAG版本。其次，构建一个基于大模型与外部工具协同的自动化靶点档案生成系统，支持访问数据库、运行代码等复杂任务，最终输出包含整合信息的生产级靶点档案，以PDF和PowerPoint格式交付。
原文摘要 · Abstract (English)
The advancement of artificial intelligence algorithms has expanded their application to several fields such as the biomedical domain. Artificial intelligence systems, including Large Language Models (LLMs), can be particularly advantageous in drug discovery, which is a very long and expensive process. However, LLMs by themselves lack in-depth knowledge about specific domains and can generate factually incorrect information. Moreover, they are not able to perform more complex actions that imply the usage of external tools. Our work is focused on these two issues. Firstly, we show how the implementation of an advanced RAG system can help the LLM to generate more accurate answers to drug-discovery-related questions. The results show that the answers generated by the LLM with the RAG system surpass in quality the answers produced by the model without RAG. Secondly, we show how to create an automatic target dossier using LLMs and incorporating them with external tools that they can use to execute more intricate tasks to gather data such as accessing databases and executing code. The result is a production-ready target dossier containing the acquired information summarized into a PDF and a PowerPoint presentation.
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